https://www.kaggle.com/new-york-state/nys-children-in-foster-care-annually https://www.ncsc.org/Microsites/EveryKid/Home/Data-and-Reform-Efforts/Data-By-State.aspx https://www.acf.hhs.gov/cb/resource/trends-in-foster-care-and-adoption
library(readxl)
#library(sf)
library(usmap)
library(tidyverse)
library(viridis)
library(rvest)
library(plotly)
library(ggsn) # for scale bar `scalebar`#national dataset
nation_data<-read_excel("data/national_afcars_trends_2009_through_2018.xlsx",sheet="Data")
#State dataset
#Numbers of Children Served in Foster Care, by State
state_served <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Served!A8:K60") %>%
gather(year,Served,'FY 2009':'FY 2018')
#Numbers of Children in Foster Care on September 30th, by State
state_inCare <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="In Care on September 30th!A8:K60") %>%
gather(year,InCare_Sep30,'FY 2009':'FY 2018')
#Numbers of Children Entering Foster Care, by State
state_entered <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Entered!A8:K60") %>%
gather(year,Entered,'FY 2009':'FY 2018')
#Numbers of Children Exiting Foster Care, by State
state_exited <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Exited!A8:K60") %>%
gather(year,Exited,'FY 2009':'FY 2018')
#Numbers of Children Waiting for Adoption, by State
state_waitingAdoption <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Waiting for Adoption!A8:K60") %>%
gather(year,Waiting_Adoption,'FY 2009':'FY 2018')
#Numbers of Children Waiting for Adoption Whose Parental Rights Have Been Terminated, by State
state_parentalRightsTerminated <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Parental Rights Terminated!A8:K60") %>%
gather(year,parental_rights_terminated,'FY 2009':'FY 2018')
#Numbers of Children Adopted, by State
state_adopted <- read_excel("data/afcars_state_data_tables_09thru18.xlsx",range="Adopted!A8:K60") %>%
gather(year,adopted,'FY 2009':'FY 2018')merge_cols<-c("State","year")
#The merge argument only takes two values as input, so you have to do them separately:
#state_df<- merge(state_served,state_inCare,state_entered,state_exited,state_waitingAdoption,state_parentalRightsTerminated,state_adopted,by=c("State","year"))
state_data<- merge(state_served,state_inCare,by=merge_cols)
state_data<- merge(state_data,state_entered,by=merge_cols)
state_data<- merge(state_data,state_exited,by=merge_cols)
state_data<- merge(state_data,state_waitingAdoption,by=merge_cols)
state_data<- merge(state_data,state_parentalRightsTerminated,by=merge_cols)
state_data<- merge(state_data,state_adopted,by=merge_cols)head(state_data)## State year Served InCare_Sep30 Entered Exited Waiting_Adoption
## 1 Alabama FY 2009 9677 6179 3080 3498 1475
## 2 Alabama FY 2010 8119 5350 3063 2770 1271
## 3 Alabama FY 2011 8395 5253 3257 3143 1297
## 4 Alabama FY 2012 7907 4561 2763 3346 1156
## 5 Alabama FY 2013 7322 4435 3041 2888 1084
## 6 Alabama FY 2014 7520 4526 3192 2994 1044
## parental_rights_terminated adopted
## 1 882 638
## 2 757 606
## 3 701 447
## 4 543 587
## 5 615 532
## 6 573 544us_map <- usmap::us_map()state_data <- state_data %>% rename(state = State)https://liuyanguu.github.io/post/2019/04/17/ggplot-heatmap-us-50-states-map-and-china-province-map/
state_data_2009 <- state_data %>% filter(year == 'FY 2009')
g <- usmap::plot_usmap(data = state_data_2009,values = "Served") +
scale_fill_viridis("Served",begin = 0.06,end=0.8,option = "plasma") +
ggtitle("Orphans Served by each state in 2009") +
theme_minimal() +
theme(legend.position = "bottom",
legend.title=element_text(size=10),
legend.text=element_text(size=5))
ggplotly(g)